Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Standing at the intersection of industry 4.0, most traditional manufacturers, especially those produce non-standard parts, are still facing the challenges from multiple aspects on the implementation of automations, that indicates a significant and necessary step towards their upgrading. The potential performance improvement that could be brought by the automation may be continuingly squeezed as the increasement of complexity when dealing with the various targets. This article is extended by a general concept of implementing automation on the metal pouring process of precision casting, aims to explore an efficient and robust automation solution with the integration of human-robots collaboration and the adoption of computer science techniques. The implementation emphasizes the reduction of unnecessary complexities from each working step, the applied algorithms, such as Object Bounding, Greedy Strategy and Last-In-First-Out, have been correspondingly tailored based on the characteristics of its engaged working steps and illustrated by the flowcharts. Both the adaptability and practicability of the automation are expected to be enhanced with the principles of constructing easy-interactive frames, allowing a certain degree of human intervention, and proactively utilizing the matured algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it